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  <div class="section" id="numpy-ma-masked-array-var">
<h1>numpy.ma.masked_array.var<a class="headerlink" href="#numpy-ma-masked-array-var" title="Permalink to this headline">¶</a></h1>
<p>method</p>
<dl class="method">
<dt id="numpy.ma.masked_array.var">
<code class="sig-prename descclassname">masked_array.</code><code class="sig-name descname">var</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">ddof=0</em>, <em class="sig-param">keepdims=&lt;no value&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/ma/core.py#L5274-L5335"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.ma.masked_array.var" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the variance along the specified axis.</p>
<p>Returns the variance of the array elements, a measure of the spread of a
distribution.  The variance is computed for the flattened array by
default, otherwise over the specified axis.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Array containing numbers whose variance is desired.  If <em class="xref py py-obj">a</em> is not an
array, a conversion is attempted.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">None or int or tuple of ints, optional</span></dt><dd><p>Axis or axes along which the variance is computed.  The default is to
compute the variance of the flattened array.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.7.0.</span></p>
</div>
<p>If this is a tuple of ints, a variance is performed over multiple axes,
instead of a single axis or all the axes as before.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">data-type, optional</span></dt><dd><p>Type to use in computing the variance.  For arrays of integer type
the default is <em class="xref py py-obj">float64</em>; for arrays of float types it is the same as
the array type.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternate output array in which to place the result.  It must have
the same shape as the expected output, but the type is cast if
necessary.</p>
</dd>
<dt><strong>ddof</strong><span class="classifier">int, optional</span></dt><dd><p>“Delta Degrees of Freedom”: the divisor used in the calculation is
<code class="docutils literal notranslate"><span class="pre">N</span> <span class="pre">-</span> <span class="pre">ddof</span></code>, where <code class="docutils literal notranslate"><span class="pre">N</span></code> represents the number of elements. By
default <em class="xref py py-obj">ddof</em> is zero.</p>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.</p>
<p>If the default value is passed, then <em class="xref py py-obj">keepdims</em> will not be
passed through to the <a class="reference internal" href="numpy.ma.var.html#numpy.ma.var" title="numpy.ma.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a> method of sub-classes of
<em class="xref py py-obj">ndarray</em>, however any non-default value will be.  If the
sub-class’ method does not implement <em class="xref py py-obj">keepdims</em> any
exceptions will be raised.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>variance</strong><span class="classifier">ndarray, see dtype parameter above</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">out=None</span></code>, returns a new array containing the variance;
otherwise, a reference to the output array is returned.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="numpy.ma.std.html#numpy.ma.std" title="numpy.ma.std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">std</span></code></a>, <a class="reference internal" href="numpy.ma.mean.html#numpy.ma.mean" title="numpy.ma.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmean</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">nanstd</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">nanvar</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">ufuncs-output-type</span></code></p>
</div>
<p class="rubric">Notes</p>
<p>The variance is the average of the squared deviations from the mean,
i.e.,  <code class="docutils literal notranslate"><span class="pre">var</span> <span class="pre">=</span> <span class="pre">mean(abs(x</span> <span class="pre">-</span> <span class="pre">x.mean())**2)</span></code>.</p>
<p>The mean is normally calculated as <code class="docutils literal notranslate"><span class="pre">x.sum()</span> <span class="pre">/</span> <span class="pre">N</span></code>, where <code class="docutils literal notranslate"><span class="pre">N</span> <span class="pre">=</span> <span class="pre">len(x)</span></code>.
If, however, <em class="xref py py-obj">ddof</em> is specified, the divisor <code class="docutils literal notranslate"><span class="pre">N</span> <span class="pre">-</span> <span class="pre">ddof</span></code> is used
instead.  In standard statistical practice, <code class="docutils literal notranslate"><span class="pre">ddof=1</span></code> provides an
unbiased estimator of the variance of a hypothetical infinite population.
<code class="docutils literal notranslate"><span class="pre">ddof=0</span></code> provides a maximum likelihood estimate of the variance for
normally distributed variables.</p>
<p>Note that for complex numbers, the absolute value is taken before
squaring, so that the result is always real and nonnegative.</p>
<p>For floating-point input, the variance is computed using the same
precision the input has.  Depending on the input data, this can cause
the results to be inaccurate, especially for <em class="xref py py-obj">float32</em> (see example
below).  Specifying a higher-accuracy accumulator using the <code class="docutils literal notranslate"><span class="pre">dtype</span></code>
keyword can alleviate this issue.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">1.25</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([1.,  1.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([0.25,  0.25])</span>
</pre></div>
</div>
<p>In single precision, var() can be inaccurate:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">512</span><span class="o">*</span><span class="mi">512</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">0.20250003</span>
</pre></div>
</div>
<p>Computing the variance in float64 is more accurate:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">0.20249999932944759 # may vary</span>
<span class="gp">&gt;&gt;&gt; </span><span class="p">((</span><span class="mi">1</span><span class="o">-</span><span class="mf">0.55</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="mf">0.1</span><span class="o">-</span><span class="mf">0.55</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
<span class="go">0.2025</span>
</pre></div>
</div>
</dd></dl>

</div>


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